# from pandas import read_csv
# from sklearn.model_selection import train_test_split
# from sklearn.linear_model import LogisticRegression
# filename = 'pima_data.csv'
# names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
# data = read_csv(filename, names=names)
# array = data.values
# X = array[:, 0:8]
# Y = array[:, 8]
# test_size = 0.33
# seed = 4
# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,
#                                                     random_state=seed)
# model = LogisticRegression()
# model.fit(X_train, Y_train)
# result = model.score(X_test, Y_test)
# print('算法评估结果： %.3f %%' % (result * 100) )
#k折交叉验证
# from pandas import read_csv
# from sklearn.model_selection import KFold
# from sklearn.linear_model import LogisticRegression
# from sklearn.model_selection import cross_val_score
# filename = 'pima_data.csv'
# names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
# data = read_csv(filename, names=names)
# array = data.values
# X = array[:, 0:8]
# Y = array[:, 8]
# num_fold = 10
# seed = 7
# kfold = KFold(n_splits=num_fold, random_state=seed, shuffle=True)
# model = LogisticRegression(multi_class='multinomial', max_iter=3000)
# result = cross_val_score(model, X, Y, cv=kfold)
# print("算法结果： %.3f%% (%.3f%%)" % (result.mean() *100, result.std() *100))
#弃一交叉验证
# from pandas import read_csv
# from sklearn.model_selection import LeaveOneOut
# from sklearn.linear_model import LogisticRegression
# from sklearn.model_selection import cross_val_score
# filename = 'pima_data.csv'
# names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
# data = read_csv(filename, names=names)
# array = data.values
# X = array[:, 0:8]
# Y = array[:, 8]
# loocv = LeaveOneOut()
# model = LogisticRegression(multi_class='multinomial', max_iter=1100)
# result = cross_val_score(model, X, Y, cv=loocv)
# print("算法评估：%.3f%% (%.3f%%)" % (result.mean()*100, result.std()*100))
#重复随机分离
from pandas import read_csv
from sklearn.model_selection import ShuffleSplit
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
filename = 'pima_data.csv'
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = read_csv(filename, names=names)
array = data.values
X = array[:, 0:8]
Y = array[:, 8]
n_splits = 10
test_size = 0.33
seed = 7
kfold = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=seed)
model = LogisticRegression(multi_class='multinomial', max_iter=1100)
result = cross_val_score(model, X, Y, cv=kfold)
print("算法评估：%.3f%% (%.3f%%)" % (result.mean()*100, result.std()*100))




















